Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms (ECGs). However, forming a CDSS for the arrhythmia classification task is challenging due to the varying lengths of arrhythmias. Although the onset time of arrhythmia varies, previously developed methods have not considered such conditions. Thus, we propose a framework that consists of (i) local temporal information extraction, (ii) global pattern extraction, and (iii) local-global information fusion with attention to perform arrhythmia detection and classification with a constrained input length. The 10-class and 4-class performances of our approach were assessed by detecting the onset and offset of arrhythmia as an episode and the duration of arrhythmia based on the MIT-BIH arrhythmia database (MITDB) and MIT-BIH atrial fibrillation database (AFDB), respectively. The results were statistically superior to those achieved by the comparison models. To check the generalization ability of the proposed method, an AFDB-trained model was tested on the MITDB, and superior performance was attained compared with that of a state-of-the-art model. The proposed method can capture local-global information and dynamics without incurring information losses. Therefore, arrhythmias can be recognized more accurately, and their occurrence times can be calculated; thus, the clinical field can create more accurate treatment plans by using the proposed method.
Enhanced ECG Arrhythmia Detection Accuracy by Optimizing Divergence-Based Data Fusion
Aerie Intelligent Technology Corp（艾瑞智能科技公司）；People’s Hospital of Shenzhen Baoan District Shenzhen（深圳市宝安区人民医院）；The Second Affiliated Hospital of Shenzhen University（深圳大学第二附属医院）
Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
University of Connecticut（康涅狄格大学）；Morehouse School of Medicine（莫尔豪斯医学院）；The Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；Department of Electrocardiogram, The First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital)（中国科学技术大学附属第一医院（安徽省立医院）心电图科）
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
University of Stavanger（斯塔万格大学）；University Medical Center Rotterdam（鹿特丹大学医学中心）；Erasmus MC Cancer Institute（伊拉斯姆斯大学医学中心癌症研究所）；Stavanger University Hospital（斯塔万格大学医院）；INCLIVA Biomedical Research Institute（INCLIVA生物医学研究所）；University of Valencia（瓦伦西亚大学）
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Universiti Malaysia Pahang（马来西亚彭亨大学）；Queensland University of Technology（昆士兰科技大学）；Islamic University（伊斯兰大学）；The University of Sydney（悉尼大学）；University of Cambridge（剑桥大学）；The Garvan Institute of Medical Research（加文医学研究所）；The University of Queensland（昆士兰大学）
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Computer Science Department, College of Science, University of Zakho（扎赫大学科学学院计算机科学系）；Department of Computer Science and Information Technology, The American University of Kurdistan（库尔德斯坦美国大学计算机科学与信息技术系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学科学研究中心PRIME实验室）
Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation
Independent Researcher（独立研究者）；Department of Materials Engineering, Xi'an Polytechnic University（材料工程系，西安理工大学）；Tandon School of Engineering, New York University（工程学院，纽约大学）；College of Computing, Georgia Institute of Technology（计算学院，佐治亚理工学院）
AeroSpectra Sentinel: An Auditable LLM Prompt-Chaining Decision-Support Workflow for Acute Asthma Risk Assessment from Respiratory Sounds and Clinical Signals
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉隆梭国际技术学院）
